ESA 2013 IPM Workshop: An Introduction to Integral Projection Models

Short Course: Modeling Biodiversity Patterns and Ecological Processes

This course is taught as a short module that introduces ecologists to Classical and Bayesian statistical models, maximum entropy models, and simulation models, originally offered in the Spring of 2011. It’s designed as a workshop using R, with a focus on Bayesian modeling and application to species distribution modeling. The original course webpage is here; this page represents an updated version.
PLEASE NOTE THAT THE LINKS BELOW WERE BROKEN WHILE REVAMPING MY WEBSITE AND WILL BE FIXED THE NEXT TIME I TEACH THE COURSE. IF YOU NEED ANYTHING BEFORE THEN, JUST EMAIL.

Course Summary

This module will explore methods for developing predictive models of biodiversity patterns and ecological processes. We will introduce the R statistical programming language and use it to demonstrate exploratory data analysis. We will cover statistical modeling (e.g. Hierarchical Bayes) as well as non-statistical modeling (e.g. Maxent), and simulation modeling (e.g. cellular automaton) approaches and demonstrate how to use these models with datasets provided by EEB faculty members and Intro IPM exercises.r. Participants will be provided with working code to help explore models on their own and under the guidance of instructors.

Course Objectives

The goal of this course is to provide students with experience using models that can be applied directly to their own research. As such we will focus on a developing a complete understanding of simpler models, as opposed to a peripheral understanding of more elaborate models. Each session will focus on one or two different modeling strategies or case studies. These meetings will be structured as a workshop with lecture interspersed with guided programming exercises. We will primarily use the statistical programming language R, and will assume that students do not have programming experience. The goal is to balance building confidence with simple models while considering models with sufficient complexity to be useful. We will provide homework assignments where participants make simple modifications to sample code from models discussed in class. Extra help will be available during regularly scheduled office hours for programming questions.

Prerequisites

As a prerequisite, students will be provided with a basic introductory packet on the statistical software package R that guides them through sample code. While the course will assume that students are unfamiliar with R, we will expect that students can work through some of the most elementary calculations on their own so that class time can be used efficiently. Students will not be expected to write their own code during the course, but will be asked to read prepared code prepared and make basic modifications.

Workshop Content

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Day 1

Goal

Introduction to R

Content

Exploratory data analysis with for community abundance patterns
Slides

Excerises

Part 1, Part 2
Data for exercises. You don't need to download these, R will do it for you.
fynbos site abundance.csv
fynbos sites.csv

Homework

Suggested Further Reading

An Ecological Modeler’s Primer on R. A tutorial on basics, entering data, functions and plotting.

icebreakeR. A clearly written and complete medium-length tutorial.

Quick-R. A very succinct guide for a handful of standard analyses. Nicely demonstrates how simply some analyses can be coded.

Day 3

Goal

Introduce concepts behind Bayesian modeling

Content

Fundamentals of Bayesian modeling (basic probability rules, Bayes’ Rule, priors and posteriors, examining model output), Building nonhierarchical regression models from scratch.
Download MCMCRobot (scroll down to the bottom of the page). This is a nice little program that Paul Lewis wrote for teaching MCMC concepts. It runs on a PC by clicking on the .exe file. Mac users will have to follow along on this one unless you have experience running pc programs through wine.
Slides
Exercises will be handed out in class
A Bayesian Jargon List to keep track of new terminology

Day 7

Goal

Use Maxent to model species distributions

Content

We will develop the general theory of Maxent, demonstrate how to interpret results and discuss important considerations about settings.
Download Maxent. This is free runs on any operating system. Make sure you've got it up and running before class.
Slides